gemseo.algos.linear_solvers.scipy_linalg.settings.base_scipy_linalg_settings module#

Settings for the SciPy linear solvers.

Settings BaseSciPyLinalgSettingsBase(*, store_residuals=False, use_ilu_precond=False, save_when_fail=False, atol=0.0, rtol=1e-12, callback=None, maxiter=1000, x0=None, M=None)[source]#

Bases: BaseLinearSolverSettings

The settings of the SciPy GMRES algorithm.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

  • store_residuals (bool) --

    By default it is set to False.

  • use_ilu_precond (bool) --

    By default it is set to False.

  • save_when_fail (bool) --

    By default it is set to False.

  • atol (Annotated[float, Ge(ge=0)]) --

    By default it is set to 0.0.

  • rtol (Annotated[float, Gt(gt=0)]) --

    By default it is set to 1e-12.

  • callback (Annotated[Callable, WithJsonSchema(json_schema={}, mode=None)] | None)

  • maxiter (Annotated[int, Gt(gt=0)]) --

    By default it is set to 1000.

  • x0 (ndarray | None)

  • M (LinearOperator | ndarray | sparray | None)

Return type:

None

M: LinearOperator | ndarray | sparray | None = None#

The preconditioner approximating the inverse of A.

atol: NonNegativeFloat = 0.0#

The absolute tolerance.

Algorithm stops if norm(b - A @ x) <= max(rtol*norm(b), atol).

Constraints:

  • ge = 0

callback: Annotated[Callable, WithJsonSchema({})] | None = None#

The user-supplied function to call after each iteration.

It is called as callback(xk), where xk is the current solution vector. If None, no function is called.

maxiter: PositiveInt = 1000#

Maximum number of iterations.

Constraints:

  • gt = 0

rtol: PositiveFloat = 1e-12#

The relative tolerance.

Algorithm stops if norm(b - A @ x) <= max(rtol*norm(b), atol).

Constraints:

  • gt = 0

x0: ndarray | None = None#

Starting guess for the solution.

If None, start from a matrix of zeros.

model_post_init(context, /)#

We need to both initialize private attributes and call the user-defined model_post_init method.

Parameters:

  • self (BaseModel)

  • context (Any)

Return type:

None